Circuit pattern inspection method and apparatus

ABSTRACT

The present invention provides techniques, including a method and system, for inspecting for defects in a circuit pattern on a semi-conductor material. One specific embodiment provides a trial inspection threshold setup method, where the initial threshold is modified after a defect analysis of trial inspection stored data. The modified threshold is then used as the threshold in actual inspection.

CROSS-REFERENCES TO RELATED APPLICATIONS

[0001] This application is a divisional application of and claimspriority to the following prior non-provisional application:

[0002] U.S. patent application Ser. No. ______ “A Circuit PatternInspection Method And Apparatus,” by Takashi Hiroi, et. al, filed Feb.22, 2001 (Attorney Docket No. 16869P-017800).

[0003] The following commonly assigned, co-pending application isincorporated by reference in their entirety:

[0004] U.S. patent application Ser. No. 09/450856, “Inspection Method,Apparatus and System for Circuit Pattern,” by Nara Yasuhiko, et. al,filed Nov. 29, 1999.

BACKGROUND OF THE INVENTION

[0005] The present invention generally relates to inspection in asemiconductor manufacturing process and in particular to using aninspection system to inspect for defects in a circuit pattern on asemiconductor material. The circuit pattern may include, a LiquidCrystal Diode (LCD) display, a Thin Film Transistor (TFT) display, amemory matte, an integrated circuit, a photomask, a magnetic head, andthe like. The inspection system may include a Semiconductor ElectronMicroscope (SEM) detection system, an optical detection system, a X-raydetection system, a Focus Beam Ion detection system, a TransparentElectron Microscope (TEM) detector system, a particle detection system,and the like.

[0006]FIG. 1 shows a simplified layout of a semiconductor wafer 100which is a target of an inspection system. There are many die, forexample, 110, 112, and 114 on the wafer 100. Normally each die has thesame pattern on the wafer 100 for use in the same product.

[0007]FIGS. 2 and 3 each show a conventional inspection system (anexample is given in U.S. Pat. No. 5,502,306, “Electron Beam InspectionSystem and Method,” by Meisburger, et. al., issued Mar. 26, 1996.Another example is given in U.S. Pat. No. 6,087,673, “Method forInspecting Pattern and Apparatus Thereof,” by Shishido, et. al., issuedJul. 11, 2000) In the conventional system, a SEM Detecting Apparatus 208is connected to an Image Processing System 228. The SEM DetectingApparatus includes, an electron beam 210 from an electron source 212sending electrons to a wafer 100 through an objective lens 214. Thesecondary electron emissions 216 from the wafer 100 are detected by asensor 218. A beam deflector 220 causes the electron beam 210 to scanhorizontally, while stage 222 movement causes a vertical scan. Thus atwo dimensional (x-y) image is obtained. The resulting analog sensorsignals are converted to digital data and this two-dimensional digitalimage is sent to Image Processing System 228 for defect detection.

[0008] In the Image Processing System 228 the first digital image isstored as a reference image. Another scan of a different potion of thewafer, produces a second digital image. This second image is theinspection image which may or may not be stored, and is compared withthe reference image. As the two images are presumed to have the samepattern, a difference image is formed. The difference image isthresholded with an initial threshold, and when a defect image exists, adefect is determined to exist. Defect information such as defectposition (x-y coordinates), size (area), x-projection size, andy-projection size is also generated. The defect information forms anentry in a defect list 232. The above process is repeated with theinspection image, i.e., second digital image, being stored as thereference image and overwriting the stored first digital image. A newlyscanned image, i.e., third digital image, is the new inspection imageand replaces the second digital image. The result of this repetitiveprocess is a defect list 232. This defect list 232 is sent by the ImageProcessing System 228 to a Graphic User Interface (GUI) Console 230 forverification by the user. If the user desires to view a defect in thedefect list 232, the positional information is used to re-scan thedefect area and show the defect on the console 230.

[0009] The conventional Image Processing System 228 normally operates inone and only one of two detection modes at a time. One is a die to diecomparison mode 234 (FIG. 2) and the other is an array comparison mode236 (FIG. 3). The die to die comparison 234 compares one die image withthe next die image, where each die belongs to the same product. Arraycomparison 236 compares a repeated pattern in, for example, a memorymatte, on a die. Thus the conventional image processing system has aproblem in that a mixture of die to die comparison and array comparisoncannot be done in one scan of the wafer.

[0010] In the conventional system determining the threshold to be usedin the difference images during actual inspection of the wafer 100 isvery important. Since the defect image is determined from thresholdingthe difference image, too low a threshold may cause many false defects.Too high a threshold may miss many actual defects. Thus setting up thethreshold is an important part of the inspection process.

[0011]FIG. 4 shows a conventional threshold setup method. At step 310the user sets a value for the initial threshold based on the user's bestguess as to the maximum noise level in the images. The initial thresholdis typically set low and is raised to a higher value when applying thissetup method. The user also selects a small region of the wafer fortrial inspection. The conventional system, using the initial threshold,determines a defect list 232, including defect information (step 320),which is sent to the Graphical User Interface (GUI) console 230 for userevaluation. At step 330, the defect information is used to re-scan thedefect locations on the wafer 100 and display the defects forverification. The user then verifies whether the defects are true orfalse defects. At step 340, if there are too few true defects or toomany false defects, the user sets a new higher threshold, and the systemgoes back to step 310. Typically this loop must be repeated one to threetimes, before a final threshold is determined. In FIG. 4 user operationis indicated by a bold box 360. Thus step 310 and 330 involve useroperation 360. This threshold setup method has several problems. Firstit is slow and manually intensive. Since defect images are not stored,rescanning is necessary to view the defect list 232 for verification. Asscanning requires wafer stage 222 movement, this process takes time. Ifthe user determines that the threshold is too low, the user must guessat a new level. The results of the threshold modification are availableonly after the small region is re-scanned during a second trialinspection. The above process is repeated several times and is slow.Another problem is that the repetitive re-scanning of the wafer 100could alter the wafer surface and hence the inspection results. Lastly,no image data is retained for use in actual inspection or follow-upanalysis, thus it is difficult to improve the process.

[0012] Therefore there is a need for a defect inspection method andsystem that is faster and more efficient. There is also a need formaintaining defect image data for use in, for example, trial inspection,defect analysis, actual inspection, and/or after inspection analysis.

SUMMARY OF THE INVENTION

[0013] The present invention provides techniques, including a method andsystem, for inspecting for defects in a circuit pattern on asemi-conductor material. One specific embodiment provides a trialinspection threshold setup method, where the initial threshold ismodified after a defect analysis of trial inspection stored data. Themodified threshold is then used as the threshold in actual inspection.

[0014] One embodiment of the present invention provides a method forinspecting a specimen, for example, a circuit pattern on a semiconductorwafer. The method includes: setting a threshold value. Next, a detectedimage of a specimen is detected and compared to a reference image; Adefect candidate is then extracted using the threshold value and aninformation of the defect candidate is stored to a memory. A newthreshold value is set and a defect is extracted from the defectcandidate using the stored information and the new threshold value. Theinformation of the defect may include at least one of the following: adefect candidate position, a defect candidate area, a defect candidate xand y projection size, a maximum difference between the detected imageand the reference image, a defect texture, or a reference texture or animage of the defect candidate.

[0015] In an alternative embodiment of the present invention a methodfor inspecting a circuit pattern on a semiconductor material isprovided. First an initial threshold is set. Next, an inspection imageis detected. A defect candidate information, for example, a margin, isdetermined by thresholding a comparison between the inspection image anda reference image, where the thresholding uses the initial threshold. Anew threshold is determined using the defect candidate information and adefect in the inspection image is evaluated using said new threshold.

[0016] Another embodiment of the present invention stores raw imagesreceived from a detector system, for example, a Semiconductor ElectronMicroscope (SEM) detecting apparatus, which scans dies on asemi-conductor wafer. An initial threshold is set from an average of theelectron beam noise. From the stored raw images, inspection images andcorresponding reference images are extracted using die to die comparisonand/or array comparison. A difference between an inspection image andits corresponding reference image is thresholded, using the initialthreshold, to determine if a potential or candidate defect exists in theinspection image. The defect is, at this stage a potential or candidatedefect, as it later will be verified by the user to be either a true ora false defect. Clipped images of the inspection and reference images,along with defect candidate information, are stored in a computerreadable medium. Next using the clipped images of the inspection andreference images, a defect distribution is obtained. Using this defectdistribution a new threshold is determined. A GUI display is providedshowing a two dimensional defect distribution with symbols representingthe defect candidates with thresholds equal to or above the newthreshold. By selecting a symbol the user can verify the defect, as atrue or false defect, in an expanded view, showing, for example, theclipped inspection image associated with the symbol. After verifying aplurality of defect candidates, the user may set another threshold, andagain view the results responsive to this other threshold. As the imagesare stored re-scanning is not necessary. The end result is a thresholdthat may be used for actual inspection. This threshold is obtainedfaster and more efficiently than the conventional method. In addition,the stored images may be used for actual inspection and after inspectionanalysis.

[0017] One embodiment of the present invention provides a method, usinga computer, for performing defect analysis on a plurality of images froman inspection system. The method includes, storing the plurality ofimages in a computer readable medium; retrieving an inspection imagefrom a first image of the stored plurality of images; retrieving acorresponding reference image from a second image of the storedplurality of images; and analyzing the inspection image andcorresponding reference image to determine if a true defect exists. Inaddition, in some cases the first and second image may be the sameimage.

[0018] A second embodiment of the present invention provides a method,using a computer, for inspecting for defects in a circuit pattern,including determining if there is a defect candidate image, bythresholding a difference image, where the difference image comprisesthe difference between an inspection image and a corresponding referenceimage. And if there is a defect candidate image, storing a clippedinspection image and a corresponding clipped reference image. Inaddition defect candidate information, including, defect candidatepositional coordinates, is stored. A margin is also determined using theclipped inspection image and the clipped reference image. Optionally,calculations for determining a classification, a threshold for a type ofdefect, or an enhanced result may be done.

[0019] A third embodiment of the present invention provides aninspection system for examining a plurality of images having potentialdefects in a circuit pattern on a semiconductor material. The systemincludes, a defect image memory for storing clipped images of theplurality of images; an image analyzer, comprising a plurality ofprocessors, coupled with the defect image memory, for analyzing theclipped images retrieved from the defect image memory; and anon-volatile storage coupled with the image analyzer for storing theclipped images and results of the analyzing.

[0020] A forth embodiment of the present invention provides a method fordetecting defects in a circuit pattern on a semiconductor material usingan inspection system. First, a plurality of scanned images from adetecting apparatus are stored. Next an inspection image and a referenceimage are determined from the plurality of scanned images based on aselection of either die to die comparison or array comparison. And usingthe inspection image and the reference image, a defect candidate imageis determined.

[0021] A fifth embodiment of the present invention provides an imageprocessing system for detecting defects in a circuit pattern on asemiconductor material using images from a detecting apparatus. Theimage processing system includes, an image memory for storing theimages; and a defect detection image processing module for detectingdefect candidate information from stored images, where the stored imagesinclude an inspection image and/or a reference image.

[0022] A sixth embodiment of the present invention provides a method fordetermining an updated threshold for use in actual inspection of asemiconductor wafer. The method includes: setting an initial threshold;determining a plurality of difference metrics using the initialthreshold; determining a difference distribution based on the pluralityof difference metrics; and determining the updated threshold based on anevaluation of the difference distribution.

[0023] A seventh embodiment of the present invention provides a methodof resetting a threshold using a display coupled with a computer. Themethod includes, displaying a first threshold value, were the firstthreshold value is used to select the defect candidate image indicationsto be shown on a defect candidate distribution screen of the display;changing the first threshold value to a second threshold value, whereinthe defect candidate image indications on the defect distribution screenchange responsive to the second threshold.

[0024] A eighth embodiment of the present invention provides a method ina computer system for determining a threshold for use in actualinspection of a semiconductor material, comprising a circuit pattern. Afirst threshold and a second threshold are displayed. In addition agraphic representation of a defect candidate image with a margin greaterthan or equal to the second threshold minus the first threshold isdisplayed; Next, when the graphic representation of the defect candidateimage is selected for expanded viewing, a clipped image associated withthe graphic representation is shown; and when the defect candidate imageis a false defect, and a predetermined number of allowable false defectsis exceeded, a new second threshold is received from the user. Theselected clipped image is selected from a group consisting of a clippedinspection image, a clipped reference image, or a clipped defectcandidate image.

[0025] A ninth embodiment of the present invention provides a method ina computer system for displaying a defect candidate, where the defectcandidate is stored in a memory. The method includes: displaying atwo-dimensional defect candidate distribution for a threshold on a firstscreen, the two-dimensional defect candidate distribution including anindication of the defect candidate; and displaying on a second screen anexpanded view of the defect candidate, responsive to a selection of theindication on the first screen.

[0026] A tenth embodiment of the present invention provides adistributed system for inspecting semiconductor circuit pattern defects.The system includes: an inspection apparatus for acquiring a pluralityof images associated with the semiconductor circuit pattern defects andfor performing defect analysis on a plurality of stored images; a serverconnected to the inspection apparatus via a communications network forstoring the plurality of images, and for providing access to theplurality of stored images; and a client computer connected to theserver and the inspection apparatus via the communications network fordisplaying a plurality of symbols associated with selected images of theplurality of stored images in response to selection of the selectedimages by the defect analysis. In an alternative embodiment the defectanalysis is performed by the server instead of the inspection apparatus.

[0027] A eleventh embodiment of the present invention provides a methodfor determining an inspection threshold used in actual defect inspectionof a semiconductor. First, a first threshold using a defect differencedistribution is calculated; next a second threshold based on said firstthreshold is stored in a computer readable medium and then used inactual inspection.

[0028] In another embodiment of the present invention a method fordetermining a selected threshold of a plurality of thresholds, where theplurality is used in actual defect inspection of a semiconductor, isprovided. The method includes: determining the plurality of thresholdsfrom a defect difference distribution; displaying to a user anindication, such as a user selectable button, for each of the pluralityof thresholds; and responsive to a user selection of a selectedthreshold, displaying symbols of defects with differences greater thanor equal to the selected threshold.

[0029] These and other embodiments of the present invention aredescribed in more detail in conjunction with the text below and attachedfigures.

BRIEF DESCRIPTION OF THE DRAWINGS

[0030]FIG. 1 shows a simplified layout of a semiconductor wafer which isa target of an inspection system;

[0031]FIGS. 2 and 3 each show a conventional inspection system;

[0032]FIG. 4 shows a conventional threshold setup method;

[0033]FIG. 5 shows a simplified block diagram of an embodiment of aninspection system of the present invention;

[0034]FIG. 6 shows an inspection system for another embodiment of thepresent invention;

[0035]FIG. 7 shows an inspection system for yet another embodiment ofthe present invention;

[0036]FIG. 8 shows a flowchart for a threshold set up method of anembodiment of the present invention;

[0037]FIG. 9 show an expanded flowchart of the initial setup and trialinspection steps of FIG. 8 of an embodiment of the present invention;

[0038]FIG. 10 gives an example of the initial setup and trialinspection;

[0039]FIG. 11 shows an expanded view of the threshold calculation stepof FIG. 8 of an embodiment of the present invention;

[0040]FIG. 12 show was an expanded view of the threshold modificationand the judgment step of FIG. 8 of an embodiment of the presentinvention;

[0041]FIG. 13 shows an alternate embodiment of the thresholdmodification and judgment step of FIG. 8;

[0042]FIG. 14 gives an example of the threshold setup method of anembodiment of the present invention;

[0043]FIG. 15 is a schematic view of a GUI display used in the ThresholdModification and Re-judgement step of FIG. 8 of an embodiment of thepresent invention;

[0044]FIG. 16 shows a GUI of another embodiment of the presentinvention;

[0045]FIG. 17 shows a GUI of yet another embodiment of the presentinvention;

[0046]FIG. 18 shows a display of an embodiment of the present inventionhaving defect candidate images classified according to type;

[0047]FIG. 19 shows a distributed system for an embodiment of thepresent invention;

[0048]FIG. 20 shows a flowchart for using a stored recipe in actualinspection of an embodiment of the present invention;

[0049]FIG. 21 shows thresholds that may be used in actual inspection2130 of an embodiment of the present invention; and

[0050]FIG. 22 shows an example of defect difference distributions of anembodiment of the present invention.

DESCRIPTION OF THE SPECIFIC EMBODIMENTS

[0051]FIG. 5 shows a simplified block diagram of an embodiment of aninspection system of the present invention. The embodiment of theinspection system includes a Defect Detection Processing Unit 410 and aDefect Image Processing Unit 430. A SEM detection apparatus 208 iscoupled with a Defect Detection Processing Unit 410. The SEM DetectionApparatus 208 is the same as in FIGS. 2 and 3. The Defect DetectionProcessing Unit 410 includes an image memory (not shown) for storing thetwo dimensional (x-y) images from the SEM Detection Apparatus 208, a dieto die comparison module 412 and an array comparison module 414. Theimages stored in image memory are raw images of the scan of the wafer100 by the SEM Detection Apparatus 208. The image memory may includeimages of an entire wafer 100 or a section of the wafer. If only asection of the wafer is included, the image memory acts as a buffer orqueue, inputting new raw images from the SEM Detection Apparatus 208 andoutputting raw images to be processed by die to die comparison module412 or the array comparison module 414. Since raw images are stored,either die to die comparison or array comparison may be done with onescan. A user via a control console inputs into the Defect DetectionProcessing Unit 410, the wafer and die layout. The input includes, a diepitch which is used in die to die comparison and a cell pitch which isused in array comparison. The die to die comparison module 412 hasfunction similar to the die to die comparison 234 performed in the ImageProcessing System 228 of FIG. 2, but in the Defect Detection ProcessingUnit 410, a defect candidate 416 is produced rather than an entry in adefect list 232. In addition the Defect Detection Processing Unit 410has an array comparison module 414 for performing an array comparisonlike 236 in Image Processing System 228 of FIG. 3, and it also producesa defect candidate 418. A defect candidate is a potential defect, whichmay be a true, i.e., actual, or a false defect. On one or more of thedefect candidates, the user will later verify, whether the defectcandidate represents a true or false defect. When a defect candidate ,for example 416 or 418, is determined to exists, clipped images,including a clipped inspection image having the defect candidate, and acorresponding clipped reference image (and optionally a clipped defectcandidate image), and defect candidate information is outputted by theDefect Detection Processing Unit 410 to the Defect Image Processing Unit430. The Defect Detection Processing Unit 410 has program code forclipping the images and for determining the defect candidateinformation, for example, defect candidate position, area, x & yprojection sizes, and optionally a margin.

[0052] The Defect Image Processing Unit 430 includes, a Defect ImageMemory 432, Multiple Processor Elements 434, a Monitor 436, and SystemSoftware 438. The Defect Image Memory 432 receives and stores theclipped images 420 and the defect candidate information from the DefectDetection Processing Unit 410. The Multiple Processor Elements 434,include one or more processors, and perform the defect analysis on thedata stored in Defect Image Memory 432 using System Software 438. Thedefect analysis results are displayed to the user on Monitor 436.

[0053]FIG. 6 shows an inspection system for another embodiment of thepresent invention. The SEM Detecting Apparatus 510 has a SemiconductorElectron Microscope (SEM) and is coupled with an Image Processing System512, which analyzes the images from the SEM. The Image Processing System512 includes an Image memory 520, a Defect Detection Image ProcessingCircuit 530, a Defect Image Memory 550, and an Image Analyzer 560. TheImage Processing System 512 is coupled with a Monitor 575 and anon-volatile Storage Medium 570. The Image Memory 520 and the DefectDetection Image Processing Circuit 530 perform functions the same as orsimilar to the Defect Detection Processing Unit 410 of FIG. 5. The ImageAnalyzer 560 has functions similar to the Multiple Processor Elements434 and System Software 438 of FIG. 5.

[0054] The SEM Detecting Apparatus 510 scans a wafer and records theimages in Image Memory 520. In order to maintain a reasonable memorysize, Image Memory 520 may be a fixed size queue or buffer. A defectcandidate is located as the result of a comparison check performed by aDefect Detection Image Processing Circuit 530 using a reference imageand an inspection image stored in the Image Memory 520. In oneembodiment the reference image is subtracted from the inspection imageand thresholded using an initial threshold th0. If there exists a binarydefect image then a defect candidate exists. The defect candidatepositional information 540 is calculated by Defect Detection ImageProcessing Circuit 530, and the Image Processing System 512 clips animage of the defect candidate out of the inspection image and clips acorresponding image out of the reference image and stores the clippedimages into a Defect Image Memory 550. An Image Analyzer 560 performsdefect analysis on the clipped images stored in the Defect Image Memory550. In one embodiment defect analysis includes determining a newthreshold th1 based on a difference distribution. The Image Analyzer 560includes a multiprocessor system, having a plurality of processors,where each processor may concurrently analyze a set of clipped images,for example, a clipped inspection image, and a clipped reference image(optionally, a clipped defect candidate image may also be included).

[0055] In one embodiment the Image Analyzer 560 calculates a defectdetection margin, herein also called “margin,” from the clippedinspection image and the corresponding clipped reference image stored inthe Defect Image Memory 550. The defect detection margin is a thresholdrange from the initial threshold level (for example, th0) to the maximumup to which the defect can be detected. By calculating the defectdetection margin per defect candidate, inspection results can be viewedas the threshold setting changes and inspection need not be conductedagain. The clipped images, defect positional information, and defectdetection margins are written onto the Storage Medium 570. Media such asDVD, CD, and HD may be used as the storage medium 570. The storagemedium may be accessed via a communications network.

[0056]FIG. 7 shows an inspection system for yet another embodiment ofthe present invention. The Optical Detecting Apparatus 610 has anoptical device used to inspect semiconductors and is coupled with anImage Processing System 612, which analyzes the images from the anOptical Inspection Apparatus 610. The Image Processing System 612includes an Image Memory 620, a Defect Detection Image ProcessingCircuit 630, a Defect Image Memory 650, and an Image Analysis System660. The Image Processing System 612 is coupled with a Monitor 675 and anon-volatile Storage Medium 670. In effect the Image Processing System612 is similar to the Image Processing System 512 for the SEM DetectingApparatus 510. Thus embodiments of the Image Processing System of thepresent invention may be used with other detecting systems, for example,a Focus Ion Beam System, or a Transparent Electron Microscope (TEM)system, and are not limited to the embodiments for the SEM or OpticalDetecting Systems described herein.

[0057]FIG. 8 shows a flowchart for a threshold set up method of anembodiment of the present invention. At step 710, the initial thresholdis set either manually by the user or automatically by the inspectionsystem. The inspection system measures the electron beam 210 noise anduses the average electron beam noise as the initial threshold, th0. Nexttrial inspection (step 720) is performed by determining defectcandidates, determining defect candidate information and images, andclipping the images. At step 730, another threshold (th1) is determinedfrom defect analysis. The user views the results of the defect analysis,including a thresholded defect candidate distribution, on a GUI displayon a monitor. Using this display the user verifies one or more of thedefect candidates and may modify the threshold th1(step 740) to, forexample, a value th2. The Image Analyzer 560 calculates a new athresholded defect candidate distribution for th2 and displays it on themonitor. As the Defect Image Memory 520 has the necessary defectcandidate images and information, the user can reset thresholds, verifydefects in images, and view the results without the need to rescan thewafer. Although optional re-scan of certain areas may be provided, it isnot necessary. Thus as shown by the bolded User Operation box 760 inFIG. 8, the user is only needed at step 740. Once the threshold isdetermined, the actual inspection (step 750) is performed. At least onestep, Threshold Modification and Re-judgement (step 740) may optionallybe used in the actual inspection (step 750) to modify the threshold. Inanother embodiment step 740 is not used and th1 is used as the thresholdfor actual inspection.

[0058] In addition as the defect candidate images and information arestored on a non-volatile storage medium 570, this data may be used forafter-inspection analysis. For example, the steps 710 to 740 can berepeated and analyzed to evaluate if a better threshold could have beendetermined. If so, then corrective measures on the process or onoperator training may be instituted.

[0059]FIG. 9 shows an expanded flowchart of the initial setup (step 710)and trial inspection (step 720) steps of FIG. 8. At process 810 theinitial threshold (th0) is set either manually by the user orautomatically using a standard metric for the system, for example, usingelectron beam noise. An inspection image 812, having a potential orcandidate defect, and a reference image 814, corresponding to theinspection image 812, are subtracted 816 from each other to give adifference image 818. The initial threshold 810 is then used tothreshold the difference image 818 (process 820) and to generate abinary defect candidate image 824 and defect candidate information 822,for example defect candidate position, defect candidate area, defectcandidate x and y projection sizes, maximum difference betweeninspection and reference images, defect texture, reference texture,average difference inside a standard circle or average difference insideseveral selected standard circles in a pattern of repeated standardcircles. In an alternative embodiment the margin may be calculated. Thedefect candidate information 822 is used at process 830 to clip theinspection image 812, the reference image 814, and the defect candidateimage 824, resulting in a clipped reference image 832, a clippedinspection image 834, and a clipped defect candidate image 836. Theclipped defect candidate image 836 is optional and is provided to assistthe user in viewing the potential defect. Clipped reference image 832and clipped inspection image 834 are used to calculate the margin inprocess 840. Note the margin could also have been calculated in analternative embodiment at process 820. At process 840 optionally othercalculations may be gone, for example, classification of the defectcandidates, thresholding of the defect candidates by a type of defect,or an enhanced result. For an enhanced result a predetermined normalthreshold thN is set. The normal threshold is greater than or equal toth0. The enhanced result for a defect candidate is the normal thresholdthN subtracted from the defect candidate's maximum threshold (i.e., thelargest threshold at which a defect candidate can be detected). Theenhanced result is similar to a normalized value. The results of process840, the defect information 822, the clipped reference image 832, theclipped inspection image 834, and the clipped defect candidate image836, are stored in a non volatile storage media (process 842). Inanother embodiment only the margin is calculated in process 840 andstored in the storage medium (process 842). The other optionalcalculations, for example, classification of the defect candidates,thresholding of the defect candidates by a type of defect, or anenhanced result are done, using, for example, the stored data in storagemedium 570, when the defect candidates are displayed, for example, inFIG. 18. In another embodiment, the only information calculated inprocess 840 is for example the first, second and third local maximums inthe difference distribution graph. These are stored in the storagemedium (process 842).

[0060]FIG. 10 gives an example of the initial setup and trialinspection. In 910 an inspection image 912 having a cross-section 914and clipped inspection image 916 are shown. The clipped inspection image916 may have dimensions of 128×128 pixels. In 910 there is a graph 920showing signal amplitude 924 versus pixel location 922 for cross-section914. A defect candidate 926 is shown on graph 920. In 930, a referenceimage 932, a cross-section 934 of the reference image 932, and a clippedreference image 936 is shown. A graph 938 shows the background noise inreference image 932 along cross-section 934. In 950, a difference image952 having a cross-section 954 is shown. The graph 956 has as its y-axis958 the difference in signal amplitude from graphs 920 and 938. Thedefect candidate signal 960 associated with the defect candidate 926 hasdefect signal maximum difference 962. The noise 964 results from thedifference in noise from inspection image 912 and reference image 932.The initial threshold th0 966 in one embodiment is manually orautomatically set to the noise 964. The margin 968 is the differencebetween the defect signal maximum difference 962 and the threshold th0966. In an 970 a defect candidate image 972, having cross-section 974,and a clipped defect candidate image 978 are shown. The defect candidateor potential defect 980 can be readily seen. In 970 a graph 982 with abinary amplitude 984 is shown for defect candidate image cross-section974.

[0061]FIG. 11 shows an expanded view of the threshold calculation step730 of FIG. 8. The clipped reference image 1010 and clipped inspectionimage 1012 are received at process 1014. These images are retrieved fromthe storage medium 570 or are used directly from FIG. 9 (images 832 and834). At process 1014, first the clipped reference image 1010 issubtracted from the clipped inspection image 1012 to obtain a differenceimage. Next a difference metric is obtained from the difference image,for example, the signal amplitude above threshold th0 for across-section of the difference image is calculated. In an alternativeembodiment the difference metric for the difference image is the margin(process 1016). The difference metric for each defect candidate is usedto determine a difference distribution over all the defect candidates(process 1018). At process 1020 a new threshold th1 is determined, forexample, using the first local minimum in the difference distribution.In another embodiment defect density, i.e., frequency per unit area,versus difference is first plotted. Next the area from a threshold thXto infinity is calculated for each difference, i.e., threshold, value.Where there is a plateau, i.e., the area does not substantially change,the defect density is determined to be stabilized and the threshold th1is set at one of the plateau values. In another embodiment a fixeddefect count or a fixed defect density may be set as th1. In yet anotherembodiment, a 3 DB point above the minimum at infinity may be set asthreshold th1 in a defect density diagram such as 1452 in FIG. 15.

[0062]FIG. 12 shows an expanded view of the threshold modification andRe-judgment step 740 of FIG. 8. At process 1110 the initial thresholdth0 and the threshold th1 calculated from step 730 are displayed. Next atwo dimensional defect candidate difference distribution is displayedusing symbols or indications representing defect candidate images withmargins greater than or equal to (th1-th0). At process 1114 a symbolrepresenting a defect candidate is selected for expanded view. One tothree images, for example the clipped reference image, the clippedinspection image, and/or the clipped defect candidate image, may bedisplayed in another screen. Optionally the defect area may be SEMre-scanned and/or optical rescanned, and the corresponding image(s)displayed. The expanded image(s) of the defect candidate is verified bythe user to be a true or false defect (process 1116). If there are moredefect candidates to check (decision 1118) then process 1114 is returnedto. If there are no more defect candidates to check then at decision1120 it is determined if there are an allowable number of false defects.If there are allowable number of false defects, then the threshold setupprocess of FIG. 8 is complete (process 1122) and actual inspection isperformed (step 750). If there are too many false defects, then atdecision 1120 a new threshold level is set for th1 by the user andprocess 1110 is repeated.

[0063]FIG. 13 shows an alternate embodiment of the ThresholdModification and Re-judgment step 740 of FIG. 8. At process 1210thresholds th0 and th1 are displayed. At process 1212 defect candidateimages with difference metric's greater than or equal to th1 aredisplayed. At process 1214 when an indication of a defect candidateimage is selected for expanded view, the associated clipped inspectionimage is displayed. At process 1216 the user views the clippedinspection image and verifies if the defect candidate is a true or afalse defect. At decision 1218 a test is made to see if there are moredefect candidates to check. If yes then the process returns to process1214. If no then the allowable number of false defects is checked(decision 1220). If there are an allowable number of false defects thenthe threshold setup process is finished ( process 1222). If there aretoo many false defects, then the threshold level th1 is set to a newvalue at process 1224. At process 1226 new defect candidate images aregenerated and the process returns to process 1210.

[0064]FIG. 14 gives an example of the threshold setup method of anembodiment of the present invention. The graph 1308 shows frequency 1310versus the difference metric 1312. Graph 1308 includes a sub-graph 1322showing a Gaussian noise distribution and sub-graph 1324 showing adefect distribution. The area 1314 under the Gaussian noise curve 1322is a normal frequency distribution without any defects. The area 1318under the defect distribution curve 1324 gives the frequency of defectsat a specified threshold. Graph 1308 represents all the differencesprior to any thresholding. Graph 1325 shows the results of the initialsetup and trial inspection steps 710 and 720 of FIG. 8. The initialthreshold th0 1326 is set. All differences below the threshold th0 hadbeen removed. The normal noise above threshold 1326 includes areas 1328and 1330. Graph 1334 shows the results of the threshold calculation step730 of FIG. 8. The new threshold th1 1340 is set as the first minimum orvalley between curve Gaussian noise curve 1322 and defect distributioncurve 1324. Graph 1342 shows the result of the Threshold Modificationand Re-judgment step 740 of FIG. 8. This step 740 may be optional, but,if it is included, it uses user selection from a GUI to modify thethreshold from threshold th1 1340 to threshold th2 1346. For thisexample the optimal threshold, thresholds out areas 1328 and 1330 asthey are noise and retains the defect area 1318. In this example theoptimal threshold is th2.

[0065]FIG. 15 is a schematic view of a GUI display used in the ThresholdModification and Re-judgement step 740 of FIG. 8 of an embodiment of thepresent invention. The GUI is used for checking inspection results aftertrial inspection and threshold calculation of th1. On a map display area1410 on the display, the small solid square marks, for example, 1412,1414, 1416, and 1420, indicate the locations of detected defectcandidates. When one of these marks (i.e., symbols) is selected, forexample, 1420, and dragged to the expanded image display area 1430, theclipped inspection image of the defect candidate stored in the defectimage memory 550 is displayed in the expanded image display area 1430. Adefect category input box, not shown on figure, is also displayed on theGUI. Defect category examples are hole missing, high impedance, foreignparticle, and short circuit. In another embodiment, the clippedinspection image, the clipped reference image, the clipped defectcandidate image or any combination thereof, may be shown in the expandedimage display area 1430. In an alternate embodiment a re-scanned SEMand/or a re-scanned optical image(s) of the defect area may bedisplayed. In a further embodiment, if these images are in the ImageMemory 520 a re-scan may be skipped and the images recalled from memory.

[0066] Buttons 1432 and 1434 allows a choice of automatic 1432 or manual1434 threshold re-setting. In this example, it is assumed that the Autobutton 1432 is chosen. On horizontal bar 1440 there is an initialthreshold of th0 that has been preset before trial inspection and onhorizontal bar 1442 there is a recommended threshold of th1 that hasbeen automatically calculated, for example at step 730 of FIG. 8. Whenthe Execute button 1444 is selected, the defect candidates which havedefect detection margins greater than or equal to (th1-th0) are shown onmap display area 1410. The values of defect count 1446 and defectdensity 1448 are also updated accordingly. In an alternative embodiment,the th1 threshold is applied to the difference of the clipped inspectionand corresponding clipped reference images stored in defect image memory550 for each defect candidate image. The plurality of processingelements in Image Analyzer 560 allow many of these calculations to occurin parallel. On the map display area 1410, the defect candidate marksrelating to the th1 threshold are shown, and the values of defect count1446 and defect density 1448 are also updated accordingly. In anotherembodiment, the defect distribution is shown for a range of margins; forexample, thL<defect detection margin<thH, where thL, thH are low andhigh thresholds, respectively.

[0067] When the Inform button 1450 is chosen, a graph 1452 showing therelation between the threshold (e.g., th0 and th1) and the defectdensity is displayed and this graph 1452 provides information that canbe used for judging whether the new threshold of th1 is proper.

[0068] If the Manual select button 1434 is chosen, the threshold th1 maybe changed by sliding the Display TH bar 1442. When the Execute button1444 is pressed after selecting another threshold, the defect candidatemarks and the values displayed in the area 1410 and the values of defectcount 1446 and defect density 1448 are updated to those in accordancewith the result of inspection to which the threshold set by the slideposition 1442 is applied. Buttons 1454 and 1456 allows the choice oftwo-value 1454 or multilevel (grayscale) 1456 for the defect candidatemarks on the map display area 1410. If multilevel 1456, is chosen, agray scale display in which the greater the defect detection margin thedarker the defect candidate mark, is presented. The multilevel displayis used for reference, when the th1 threshold is manually set and showshow dark defect candidates and light defect candidates are distributedon the wafer. In another embodiment a color code, mark size, mark shapecode may be used instead of the grayscale. In yet another embodiment thegreater the difference above the threshold th0 (i.e., the greater thedefect detection margin) the lighter the defect candidate mark. Forexample, if the difference represented electrical resistance then thelighter the defect candidate mark, the lower the resistance. A verylight mark may indicate a short circuit, while a very dark mark an opencircuit.

[0069] Create Recipe button 1470 allows the use of a recipe or programscript that sets the inspection mode in either die to die or array forvarious sections of the wafer 100. The Inspect button 1472 allows use ofthis GUI in actual inspection. And the button Check Defect allows use ofthis GUI in after inspection analysis.

[0070] According to this example, the user can easily view trialinspection results after threshold setting change without conducting thetrial inspection again as in the conventional system, and therefore cangreatly save time as compared with conducting the inspection again. Inaddition, this threshold setting process may be used during actualinspection to make adjustments. Thus this method is more flexible.

[0071] As the clipped images are stored in storage medium 570, it isalso possible to do after inspection analysis of the defects. Thus thedefect inspection process can be examined for improvements. Data is alsoavailable to assist in determining future initial threshold values. Thusefficiency may be improved.

[0072]FIG. 16 shows a GUI of another embodiment of the presentinvention. In this embodiment the user can select which sections of thedefect distribution screen 1510 uses what threshold. For example inscreen 1510, there are two concentric circle areas shown, the outercircle 1512 and the inner circle 1515. A defect candidate 1520 in outercircle 1512 may be thresholded for display at a different threshold thandefect candidate 1525 in the inner circle 1515. Threshold bars, forexample, 1440 and 1442 could be assigned to the outer circle 1512 andinner circle 1515, accordingly.

[0073]FIG. 17 shows a GUI of yet another embodiment of the presentinvention. In this embodiment the user can select an arbitrary section1554 (dark dotted area) of the defect distribution screen 1550 for usewith one threshold, while the remainder of the screen 1552 uses anotherthreshold. The area may be selected by use of a mouse outlining the areato be selected. A defect candidate 1560 may be thresholded for displayat a different threshold than defect candidate 1565 in selected area1554. Threshold bars, for example, 1440 and 1442 could be assigned tothe selected area 1554 and the remainder 1552 accordingly.

[0074] In another embodiment of the present invention, the imageanalyzer 560 determines distinctive features of a defect candidate, forexample, its lightness, circumference, boundary unevenness, orientation,and position on the background pattern and then uses these features toclassify the type, for example, open contact hole, short-circuit,foreign particle, or thin film residue, of the defect candidate image.The present embodiment is particularly intended to enable the user toview the results of inspection per defect type and to allow the user toset individual thresholds based on defect type.

[0075] An example of how to classify defects is described below. If, forexample, holes of a memory device are assumed to be inspected, an opencontact hole (open circuit) tends to look darker than a normal hole anda short-circuited hole tends to look lighter than a normal hole. Theimage analyzer 560 determines average lightness of a defect location asa distinctive feature of the defect by using the margin. Using thisfeature, the image analyzer 560 classifies the defect candidate as opencontact hole or short-circuit. In an alternative embodiment the imageanalyzer 560 determines average lightness of a defect location as adistinctive feature of the defect by using the clipped inspection imageand the corresponding clipped reference image stored in the defect imagememory 550. In another embodiment, there are two defect distributiondisplay formats and the GUI has a toggle button to switch between thetwo formats. One format is an automatically classified defect type andthe other format is a manually classified defect type.

[0076]FIG. 18 shows a display of an embodiment of the present inventionhaving defect candidate images classified according to type. On the mapdisplay area 1610, different marks or symbols for different defect typesindicate the detected defects classified into four types: open contacthole 1620, short-circuit 1622, foreign particle 1624, and thin filmresidue 1626. In display area 1610 are shown examples of an open contacthole defect candidate 1632, a short-circuit defect candidate 1634, aforeign particle defect candidate 1636, and thin film residue defectcandidate 1638.

[0077] The defect type (thresholded) button 1462 is assumed to beselected for this display. If the common to all defects button 1460 isselected then only a common defect is shown for all defect candidatesand the display looks more like 1410 of FIG. 15 in format.

[0078] When the automatic threshold re-setting method is chosen by usingthe button 1432, new thresholds for all defect types are calculated andthe results are shown on the horizontal bars 1630, 1632, 1634, and 1636for the defect types 1620, 1622, 1624, and 1626 respectively. Theautomatic new threshold calculation method is the same as described forthe previously embodiment for one type. On the other hand, if the Manualbutton 1434 is chosen, the desired thresholds for all defect types maybe set by the user by sliding the horizontal bars 1630, 1632, 1634, and1636 for the defect types.

[0079] A table 1640 allows the selection of a display view per defecttype for displaying the defect candidate symbols on the map. That is foreach defect type, a mutually exclusive choice of “off,” two-value,” or“gray” may be selected. If you choose “off” for a first defect type,defect locations classified into the first defect type are not displayedon the map. If you choose “two-value” for a second defect type, atwo-value mark, for example, binary, is displayed for defect candidatesof that type on the map. If you choose multilevel for a third type , themarks of the defect candidate for the third type, become darker orlighter, according to the defect detection margin specific to anindividual defect candidate when being displayed. In another embodimentall defect types may be “off,” “two-valued,” or “gray” together. Inanother embodiment, a multi level display would include a circle, ifth0<defect margin<th1, a triangular, if th1<defect margin<th2, or arectangular, if th2<defect margin<th3.

[0080] The present embodiment enables the user to set inspectionsensitivity, according to the defect type, so that inspection of alldefect types with sensitivity suitable for each of the types can beconducted. This can solve the problem that inspecting defects of onetype results in the detection of too many defects, because thesensitivity for detecting another defect type is too high.

[0081]FIG. 19 shows a distributed system for an embodiment of thepresent invention. The GUI display 1810 as shown, for example, in FIG.15, may run on a Personal Computer (PC) 1820 as a client program. The PC1820 is connected with a server 1830, having a DataBase (DB) 1832 via aCommunications Network 1835. The Communications Network 1835 may be, forexample, an intranet, Local Area Network (LAN), or the Internet. TheInternet may be used if the analysis facility, having the PC 1820 andServer 1830, are located in one location, for example one country, andthe manufacturing facility, having the Inspection Apparatuses 1842 and1844, are in another location, for example another country. The DB 1832includes the information stored in the storage medium by process 842 ofFIG. 9, for example, the clipped images, margin, and the defectcandidate information. The DB 1832 may serve as the storage medium 570in FIG. 6. The Server 1830 then provides the images and data to the PC1820, Review Apparatus 1840, Inspection Apparatus 1842, and InspectionApparatus 1844 via Communications Network 1835. Thus defect images,information, and defect detection margins stored in a storage medium,i.e., DB 1832 can be referenced from anywhere via the CommunicationsNetwork 1835.

[0082] The Image Processing System, such as 512 in FIG. 6, is inInspection Apparatus 1842. Inspection Apparatus 1842 further includes adetecting apparatus, for example, a SEM Detecting Apparatus 510.Inspection Apparatus 1844 may have a SEM Detecting Apparatus 510 or anOptical Detecting Apparatus 610 or a combination as shown in U.S. Pat.No. 6,087,673, “Method for Inspecting Pattern and Apparatus Thereof,” byShishido, et. al., issued Jul. 11, 2000. Review Equipment 1840, which isoptional, also has a detecting apparatus and in addition, a computer, torescan a wafer off-line from the manufacturing process. Review Equipment1840 is used to analyze previous defect candidate verificationjudgements and/or to classify the defects. In an alternative embodiment,the Server 1830, rather than Inspection Apparatus 1842, includes theImage Processing System, for example, 512 of FIG. 6 or Defect ImageProcessing Unit 430 of FIG. 5. The DB 1832 can store besides the storagemedium 570 contents, also the Image Memory 520, and/or Defect ImageMemory 550 data. The Server 1830 may also include Multiple ProcessorElements 434.

[0083]FIG. 20 shows a flowchart for using a stored recipe in actualinspection of an embodiment of the present invention. Steps 2010 and2020 include the steps 710 to 740 of FIG. 8. As illustrated by theexample of FIG. 14, the result of threshold modification, andre-judgement (step 740), is threshold th2. By selecting Create Recipe1470 in FIG. 15, this single threshold th2 can be stored in aninspection recipe (step 2030). Other examples of stored inspectionrecipes are Multi thresholds th21, th22, . . . th2N, and. automaticthreshold (step 2030). Also a mixed mode of the above two or threerecipe examples can be used. Using the stored recipe actual inspectionis performed (step 2040) by selecting the Inspect 1472 button in FIG.15.

[0084]FIG. 21 shows thresholds that may be used in actual inspection2130 of an embodiment of the present invention. Referring to FIG. 21,Inspection Threshold 2110 corresponds to the bar in Inspect TH 1440 inFIG. 15 and Display Threshold 2112 corresponds to the bar in Display TH1442 in FIG. 15. As indicated above, the results of threshold setup 2120is an inspection threshold 2110 of th0 2122 and a display threshold 2112of th2 2124.

[0085] In one embodiment of using the single threshold, th2, recipe, inactual inspection the threshold of the defect detection image processingcircuit 530 is fixed at th2 and no image analyzer unit 560 is used. Thusthe display threshold 2112 is essentially fixed and not user modifiable.In another embodiment the threshold of the defect detection imageprocessing circuit 530 , i.e., inspection threshold 2110, is set at(th2-α) 2132, where α is an instrument constant, for example, about 3 to6 times the standard deviation of the electron beam noise. The displaythreshold is then th2 2134 and is user modifiable during actualinspection 2130.

[0086] Using the multi threshold recipe, the threshold of defectdetection processing unit is ((minimum th1, th2, . . . , thN) -α) 2136and the display threshold 2112 shown on the GUI as buttons th21 2138,th22 2140, or thN 2142. In this case on actual inspection 2130, theoperator can either select one of the buttons th21, th22, . . . , th2Nor modify the threshold using the Display TH bar 1442 of FIG. 15, whichis initially set to the selected button. Outputs of multi thresholdmeans plural inspection results can be obtained at the same time.

[0087]FIG. 22 shows an example of defect difference distributions of anembodiment of the present invention. The axes are difference 2212, forexample signal amplitude difference between the inspection and referenceimages, and frequency 2214. The distribution 2220 represents differencesfor normal background noise. The distribution curve 2222 illustratesslight defect differences, and the distribution curve 2224 illustrateslarge defect differences. In this embodiment the result of step 1020 ofFIG. 11 is not one threshold but many, th11, th12, or th1N. Thesethresholds are automatically calculated by step 730 of FIG. 8 andrepresent the local minimums of the difference distribution. DuringThreshold Modification and Re-judgement (step 740), these may bedirectly used as th21, th22, or th2N, respectively or user modifiedsimilar to the example shown in FIG. 14 to give th21, th22, or th2N.From FIG. 22 if the operator selects threshold th21 2230, then slightdefect differences 2222 can be detected along with many false defects(high sensitivity). If threshold th22 2232 is chosen, then only defectswith large differences 2224 are detected (low sensitivity). During thesetup phase, slight difference detection, for example threshold th21, isused.

[0088] Using automatic thresholding 2146, (th2-α) 2144 is used as theinspection threshold 2110 (i.e., initial setup threshold in step 710),and the same threshold calculation given in Threshold Calculation step730 is applied to determine the threshold 2146 for actual inspection2130.

[0089] Another embodiment of the present invention provides for acomputer program product stored on a computer readable medium forinspecting a specimen. The program includes: code for setting athreshold value; code for detecting a detected image of a specimen; codefor comparing the detected image to a reference image; code forextracting from the comparing, a defect candidate using the thresholdvalue; code for storing an information of the defect candidate to amemory; code for setting a new threshold value; and code for extractinga defect from the defect candidate using the stored information and thenew threshold value.

[0090] In yet another embodiment a computer program product stored on acomputer readable medium for inspecting a circuit pattern on asemiconductor material is provided. The program includes: code forsetting an initial threshold; code for detecting an inspection image;code for determining defect candidate information by thresholding acomparison between said inspection image and a reference image, whereinsaid thresholding uses said initial threshold; code for determining anew threshold using said defect candidate information; and code forevaluating a defect in said inspection image using said new threshold.

[0091] Although the above functionality has generally been described interms of specific hardware and software, it would be recognized that theinvention has a much broader range of applicability. For example, thesoftware functionality can be further combined or even separated.Similarly, the hardware functionality can be further combined, or evenseparated. The software functionality can be implemented in terms ofhardware or a combination of hardware and software. Similarly, thehardware functionality can be implemented in software or a combinationof hardware and software. Any number of different combinations can occurdepending upon the application.

[0092] Many modifications and variations of the present invention arepossible in light of the above teachings. Therefore, it is to beunderstood that within the scope of the appended claims, the inventionmay be practiced otherwise than as specifically described.

What is claimed is:
 1. A method, using a computer, for performing defectanalysis on a plurality of images from an inspection system, comprising:storing said plurality of images in a computer readable medium;retrieving an inspection image from a first image of a stored pluralityof images; retrieving a corresponding reference image from a secondimage of said stored plurality of images; and analyzing said inspectionimage and said corresponding reference image to determine if a defectexists.
 2. The method of claim 1 wherein said first image and saidsecond image are a same image.
 3. The method of claim 1 wherein saidstored plurality of images are clipped images.
 4. The method of claim 1wherein said analyzing includes displaying said inspection image.
 5. Themethod of claim 1 wherein said analyzing occurs during actualinspection.
 6. The method of claim 1 wherein said analyzing occurs inafter inspection follow-on analysis.
 7. The method of claim 1 whereinsaid plurality of images are images from die to die comparisons, arraycomparisons, or both.
 8. A method, using a computer, for inspecting fordefects in a circuit pattern, comprising: determining if there is adefect candidate image, by thresholding a difference image, wherein saiddifference image comprises a difference between an inspection image anda corresponding reference image; if there is said defect candidateimage, storing a clipped inspection image in a computer readable medium,wherein said clipped inspection image is a portion of said inspectionimage; and if there is said defect candidate image, storing acorresponding clipped reference image in said computer readable medium,wherein said corresponding clipped reference image is a portion of saidcorresponding reference image.
 9. The method of claim 8 furthercomprising storing a clipped defect candidate image, when there is saiddefect candidate image.
 10. The method of 8 further comprising, whenthere is said defect candidate image, storing defect information,comprising defect candidate positional coordinates, in said computerreadable medium.
 11. The method of 8 further comprising, when there issaid defect candidate image, determining a margin.
 12. The method ofclaim 8 further comprising: using said clipped inspection image and saidclipped reference image to determine a margin; and storing said marginin said computer readable medium.
 13. The method of claim 8 furthercomprising: determining a classification, a threshold for a type ofdefect, or an enhanced result based in part on said clipped inspectionimage and said clipped reference image.
 14. The method of claim 13wherein said determining further uses defect information.
 15. Aninspection system for examining a plurality of images showing potentialdefects in a circuit pattern on a semiconductor material, comprising: adefect image memory for storing clipped images of said plurality ofimages; an image analyzer, comprising a plurality of processors, coupledwith said defect image memory, for analyzing said clipped imagesretrieved from said defect image memory; and a non-volatile storagecoupled with said image analyzer for storing said clipped images andresults of said analyzing.
 16. The inspection system of claim 15 whereinsaid results are defect detection margins.
 17. The inspection system ofclaim 15 wherein non-volatile storage further stores defect information.18. The inspection system of claim 15 wherein said plurality of imagescomprise an inspection image and a corresponding reference image.
 19. Amethod for detecting defects in a circuit pattern on a semiconductormaterial using an inspection system comprising: storing a plurality ofscanned images from a detecting apparatus; determining an inspectionimage and a reference image from said plurality of scanned images basedon a selection of either die to die comparison or array comparison; andusing said inspection image and said reference image, determining adefect candidate image.
 20. The method of claim 19 further comprising:when then there is said defect candidate image, clipping a first areafrom said inspection image and a corresponding second area from saidreference image.
 21. The method of claim 20 further comprising: sendinga first clipped image, comprising said first area, for storage in adefect image memory.
 22. The method of claim 19 wherein said selectionis different for different areas of a wafer.
 23. An image processingsystem for detecting defects in a circuit pattern on a semiconductormaterial using images from a detecting apparatus, said image processingsystem comprising: an image memory for storing said images; and a defectdetection image processing module for detecting defect candidateinformation from stored images, wherein at least one of said storedimages includes an inspection image.
 24. The image processing system ofclaim 23 wherein said at least one of said stored images furtherincludes a related reference image.
 25. The image processing system ofclaim 23 wherein said defect candidate information, comprising defectcandidate positional information, is used by said defect detection imageprocessing module to clip said inspection image and said relatedreference image.
 26. The image processing system of claim 23 furthercomprising an overall control for determining when die to die comparisonor array comparison is used on said inspection image and said relatedreference image.
 27. A method using a computer for determining anupdated threshold for use in actual inspection of a semiconductor wafer,said method comprising: setting an initial threshold; determining aplurality of difference metrics using said initial threshold;determining a difference distribution based on said plurality ofdifference metrics; and determining said updated threshold based on anevaluation of said difference distribution.
 28. The method of claim 27wherein a difference metric of said plurality of difference metricscomprises thresholding a difference between a clipped inspection imageand a clipped reference image.
 29. The method of claim 27 wherein adifference metric of said plurality of difference metrics comprises amargin.
 30. The method of claim 27 wherein a difference metric of saidplurality of difference metrics comprises a signal amplitude differencebetween a maximum signal value of a cross-section of a difference imageand said initial threshold, said difference image comprising subtractinga clipped reference image from a clipped inspection image.
 31. Themethod of claim 27 wherein said evaluation of said differencedistribution comprises, finding a minimum in the differencedistribution.
 32. The method of claim 27 wherein said evaluation of saiddifference distribution comprises finding a stabilized area under saiddifference distribution.
 33. The method of claim 27 wherein saidevaluation of said difference distribution comprises using a fixed valuefor a defect count or defect density.
 34. A method of resetting athreshold using a display coupled with a computer, said methodcomprising: displaying a first threshold value on said display, saidfirst threshold value used to select defect candidate image indicationsto be shown on a defect candidate distribution screen of said display;changing said first threshold value to a second threshold value, whereinsaid defect candidate image indications on said defect distributionscreen change responsive to said second threshold value.
 35. The methodof claim 34 further comprising: selecting a selected indication of saiddefect candidate image indications; and viewing an inspection imageassociated with said selected indication.
 36. The method of claim 34wherein said first threshold is calculated using an electron beam noisevalue for a SEM system.
 37. A method in a computer system fordetermining a threshold for use in actual inspection of a semi-conductormaterial, comprising a circuit pattern, said method comprising:displaying a first threshold and a second threshold; displaying agraphic representation of a defect candidate image with a margin greaterthan or equal to said second threshold minus said first threshold; whensaid graphic representation of said defect candidate image is selectedfor expanded viewing, displaying a clipped image associated with saidgraphic representation; and when said defect candidate image is a falsedefect, and a predetermined number of allowable false defects isexceeded, receiving a new second threshold.
 38. The method of claim 37wherein said clipped image is selected from a group consisting of aclipped inspection image, a clipped reference image, or a clipped defectcandidate image.
 39. A method in a computer system for displaying adefect candidate, said defect candidate stored in a memory, said methodcomprising: displaying a two-dimensional defect candidate distributionfor a threshold on a first screen, said two-dimensional defect candidatedistribution comprising an indication of said defect candidate; anddisplaying on a second screen an expanded view of said defect candidate,responsive to a selection of said indication on said first screen. 40.The method of claim 39 wherein said expanded view comprises an imageassociated with said defect candidate and selected from a groupconsisting of a clipped inspection image, a clipped reference image, ora defect candidate image.
 41. The method of claim 39 wherein saidexpanded view comprises a re-scanned image of said defect candidate. 42.The method of claim 39 further comprising a threshold screen forchanging said threshold.
 43. The method of claim 39 further comprising ascreen displaying a graph of defect density versus threshold.
 44. Themethod of claim 39 wherein said two-dimensional defect candidatedistribution displays defect candidates responsive to a user selectedarea.
 45. The method of claim 39 wherein said two-dimensional defectcandidate distribution displays defect candidates by type of defect. 46.The method of claim 45 wherein each type of defect has a differentsymbol, said defect being displayed using a symbol.
 47. The method ofclaim 45 wherein each type of defect has an associated threshold value.48. The method of claim 39 wherein said two-dimensional defect candidatedistribution displays defect candidates as symbols.
 49. The method ofclaim 48 wherein a symbol of said symbols comprise a grayscale value.50. The method of claim 49 wherein said grayscale value is related to amargin.
 51. The method of claim 49 wherein said grayscale value isrelated to an enhanced result.
 52. The method of claim 48 wherein asymbol of said symbols comprise a color value.
 53. The method of claim48 wherein a symbol of said symbols comprise a black or a white value.54. A system for displaying a symbol associated with a defect candidateof said plurality of defect candidates, comprising: a computer readablemedium for storing images associated with said plurality of defectcandidates, wherein said images comprise an inspection image and areference image associated with said defect candidate; a processorcoupled with said computer readable medium for determining a marginassociated with said defect candidate, said margin calculated using saidinspection image and said reference image; and a display for displayingsaid symbol when said margin is equal to or above a thresholddifference.
 55. The system of claim 54 wherein said threshold differenceis a difference between a display threshold value and a predeterminedinitial threshold value.
 56. A distributed system for inspectingsemiconductor circuit pattern defects, comprising: an inspectionapparatus for acquiring a plurality of images associated with saidsemiconductor circuit pattern defects and for performing defect analysison a plurality of stored images; a server connected to said inspectionapparatus via a communications network for storing said plurality ofimages, and for providing access to said plurality of stored images; anda client computer connected to said server and said inspection apparatusvia said communications network for displaying a plurality of symbolsassociated with selected images of said plurality of stored images inresponse to selection of said selected images by said defect analysis.57. The distributed system of claim 56 wherein said communicationsnetwork comprises an Internet.
 58. The distributed system of claim 56wherein said communications network comprises a Local Area Network. 59.The distributed system of claim 56 wherein said client computer furtherdisplays an image of said selected images in response to a userselection of an associated symbol of said plurality of symbols.
 60. Thedistributed system of claim 56 wherein said defect analysis comprisescalculation of a margin.
 61. The distributed system of claim 56 whereinsaid defect analysis comprises threshold recalculation.
 62. Adistributed system for inspecting semiconductor circuit pattern defects,comprising: an inspection apparatus for acquiring a plurality of imagesassociated with said semiconductor circuit pattern; a server coupledwith said inspection apparatus via a communications network, said serveroperably disposed for: storing said plurality of images; performingdefect analysis on a plurality of stored images; and providing access tosaid plurality of stored images; and a client computer coupled with saidserver via said communications network for displaying a plurality ofsymbols associated with selected images of said plurality of storedimages in response to selection of said selected images by said defectanalysis.
 63. A method for determining an inspection threshold used inactual defect inspection of a semiconductor, said method comprising:calculating a first threshold using a defect difference distribution;storing a second threshold based on said first threshold in a computerreadable medium; and using said second threshold in actual defectinspection.
 64. The method claim 63 wherein said computer readablemedium includes a recipe comprising said second threshold.
 65. Themethod claim 63 wherein said first threshold is greater than apredetermined initial threshold.
 66. A method for determining a selectedthreshold of a plurality of thresholds, said plurality of thresholds foruse in actual defect inspection of a semiconductor, said methodcomprising: determining said plurality of thresholds from a defectdifference distribution; displaying to a user an indication for each ofsaid plurality of thresholds; and responsive to said user selection of aselected threshold of said plurality of thresholds, displaying symbolsof defects with differences greater than or equal to said selectedthreshold.
 67. The method of claim 66 wherein said determining saidplurality of thresholds is based on one or more local minimums in saiddefect difference distribution.
 68. A system for determining a firstthreshold for use in actual inspection of circuit pattern defects in asemiconductor material, said system comprising: a defect detection unitfor determining defects with differences above a second threshold minusa predetermined value; and a display having an input mechanism foradjusting said first threshold, wherein said first threshold has aninitial value of said second threshold.
 69. The method of claim 68wherein said second threshold is related to a defect differencedistribution.
 70. An image processing system for determining a newthreshold for use in inspection of a circuit pattern on a semiconductormaterial, comprising: a defect detection unit for determining aplurality of defect sets, wherein a defect set of said plurality ofdefect sets comprises an inspection image and a reference image with adifference above a predetermined threshold ; and an image analysis unitfor using said plurality of defect sets to determine a differencedistribution and for using said difference distribution to determinesaid new threshold.
 71. The image processing system of claim 70 whereinat least one image in said defect set is a clipped image.
 72. The imageprocessing system of claim 70 further comprising a defect image memoryfor storing said defect set.
 73. The image processing system of claim 70further comprising an image memory comprising said reference image andsaid inspection image.
 74. The image processing system of claim 70further comprising a storage medium for storing said defect set.
 75. Amethod for inspecting a specimen, comprising: setting a threshold value;detecting a detected image of a specimen; comparing the detected imageto a reference image; extracting from the comparing, a defect candidateusing the threshold value; storing an information of the defectcandidate to a memory; setting a new threshold value; and extracting adefect from the defect candidate using the stored information and thenew threshold value.
 76. A method of inspecting a specimen according toclaim 75, wherein the information of the defect candidate stored in thememory includes at least one of the following: a defect candidateposition, a defect candidate area, a defect candidate x and y projectionsize, a maximum difference between the detected image and the referenceimage, a defect texture, or a reference texture.
 77. A method ofinspecting a specimen according to claim 75, wherein the information ofthe defect candidate stored in the memory includes an image of thedefect candidate.
 78. The method of claim 75 wherein the specimenincludes a circuit pattern on a semiconductor wafer.
 79. A method forinspecting a circuit pattern on a semiconductor material comprising:setting an initial threshold; detecting an inspection image; determiningdefect candidate information by thresholding a comparison between saidinspection image and a reference image, wherein said thresholding usessaid initial threshold; determining a new threshold using said defectcandidate information; and evaluating a defect in said inspection imageusing said new threshold.
 80. The method of claim 79 wherein said defectcandidate information includes a margin.
 81. A computer program productstored on a computer readable medium for inspecting a specimen,comprising: code for setting a threshold value; code for detecting adetected image of a specimen; code for comparing the detected image to areference image; code for extracting from the comparing, a defectcandidate using the threshold value; code for storing an information ofthe defect candidate to a memory; code for setting a new thresholdvalue; and code for extracting a defect from the defect candidate usingthe stored information and the new threshold value.
 82. A computerprogram product stored on a computer readable medium for inspecting acircuit pattern on a semiconductor material comprising: code for settingan initial threshold; code for detecting an inspection image; code fordetermining defect candidate information by thresholding a comparisonbetween said inspection image and a reference image, wherein saidthresholding uses said initial threshold; code for determining a newthreshold using said defect candidate information; and code forevaluating a defect in said inspection image using said new threshold.